Uniform Set Research Articles

Prevalence of Cognitive Domain Impairments in Mild Cognitive Impairment Subtypes in the NACC Database

AbstractBackgroundMild cognitive impairment (MCI) reflects cognitive deficits without significant functional decline in activities of daily living. MCI subtypes correspond to phenotypic cognitive impairment patterns in memory, attention, language, executive, and visuospatial compared to age‐adjusted norms: Amnestic single domain (ASD) or multi‐domain (AMD), Nonamnestic single domain (NASD), or Nonamnestic multi‐domain (NAMD. In this study, we explore frequencies of domain impairment by MCI subtype, and frequency of each combination of (memory ±) all combinations of Language, Attention, Executive and Visuospatial impairment in the NACC database. This is of practical importance for both localization of amyloid or tau deposition as well as relative localization of areas of deficit.MethodUsing the National Alzheimer’s Coordinating Center (NACC) database (grant U24 AG072122), we selected all initial Uniform Data Set (UDS) assessments diagnosed as MCI with sub‐typing. The NACC data was collected from 37 participating Alzheimer’s disease Research Centers. 9852 individuals with MCI on baseline UDS visits were analyzed: 45.4% AMD, 33.7% ASD, 12.6% NASD, 8.2% NAMD.ResultFigure one shows the distribution overall of domain specific impairment by MCI subtype. Except for ASD, Executive function was the most common domain affected, and visuospatial was the least common. NAMD had higher percentages affected for all domains compared to AMD. In Figure 2, all combinations of language, executive, attention and visuospatial are compared. The most frequent combination was Language‐Executive, and the least common combination was Language‐Attention‐Visuospatial. The mean absolute percentage difference between AMD and NAMD was 2.2%. The Spearman’s rho of AMD vs. NAMD was 0.845.ConclusionWithin an MCI subtype, there is substantial variability in the frequency of individual domains. However, comparison of AMD and NAMD shows striking uniformity in frequency of combinations of individual domains with very correlation. The latter findings suggests that Amnestic and Nonamnestic multidomain subtypes may be closely related in pathophysiology but show greater heterogeneity within a MCI subtype.

Harmonization of amyloid and tau PET tracers in a multi‐center cohort from NIA Alzheimer’s disease research centers in the United States

AbstractBackgroundThe Standardized Centralized Alzheimer’s & related dementias Neuroimaging (SCAN) project harmonizes processing of prospectively acquired PET and MRI scans from Alzheimer’s disease research centers (ADRCs) across the USA. SCAN data will be integrated with other ADRC data streams within the National Alzheimer’s Coordinating Center (NACC), including the Uniform Data Set which includes rich longitudinal demographic, cognitive, and neuropathological data, resulting in a large, potentially representative cohort. The project has sought to accommodate site‐to‐site variation in image acquisition while permitting harmonization of imaging data, necessitating new analytic approaches for different PET tracers and PET scans without MRIs.MethodPET scans underwent preprocessing for quality control and smoothing to a uniform resolution. MRI‐free processing pipelines and positivity thresholds were developed for each beta‐amyloid (Aß)‐PET tracer: [11C]PiB, [18F]florbetaben (FBB), [18F]florbetapir (FBP) and [18F]flutafuranol (NAV). A generic Aß‐PET template was used for spatial normalization and cortical summary SUVRs were calculated in template space. Aß‐positivity thresholds were determined by examining both MRI‐dependent thresholds transformed into MRI‐free units and a young control mean+2SD. Centiloid (CL) conversions were completed for each Aß‐PET tracer. Tau‐PET tracers ([18F]flortaucipir (FTP) and [18F]MK‐6240) required tracer‐specific warping templates for best performance relative to gold‐standard MRI‐dependent processing. Demographic and clinical data were obtained from NACC.Result15 ADRCs have contributed data to SCAN as of January 2023. 477 participants had preprocessed, SCAN‐compliant (https://scan.naccdata.org/) Aß‐PET scans and NACC data; 219 of these participants (46%) also had tau‐PET scans (Table 1). This cohort included 18% of individuals from underrepresented groups. Clinical diagnostic groups ranged from cognitively unimpaired to MCI and dementia (74% of whom had AD dementia). Using pooled Aß‐PET data across 4 tracers in CLs, there were expected differences in Aß burden by diagnosis group (Fig. 1) and positive associations with tau (Fig. 2).ConclusionSCAN has successfully harmonized PET data from more than a dozen US ADRCs yielding a large, clinically heterogeneous cohort of deeply phenotyped research participants. SCAN data will be compatible with ADNI data allowing for further pooling of data and greater statistical power to drive critical questions in AD and related dementias research.

The Evolution of Generally Accepted Accounting Principles: A Focused Look into the International Financial Reporting Standards

Financial reporting has long been an essential lens through which shareholders assess the managements operation of companies and through which external users such as potential investors evaluate how companies are structured and perform financially for economic decision-making. For this reason, the truth and fairness of financial statements is emphasized in financial accounting to reflect the economic substance over the legal form. As cross-border trades and investments grew by leaps and bounds after the World War II, a call for a globally uniform and consistent set of accounting standards thrived in full swing. Hence, the genesis of IAS and subsequently IFRS for better quality, comparability, understandability, and transparency of financial statements. Over recent years, the trend in accounting harmonization with gradual convergence between IFRS and US GAAP has also become prominent. However, accounting standards are never perfect in presenting economic reality upon their inception. Blatant financial scandals play an indispensable role in shaping accounting standards for improvements. This paper takes a focused look into IFRS to examine how the generally accepted accounting principles evolve in reaction to scandals and loopholes criticized, with a detailed analysis of the standards for leases and revenue recognition. A generic discussion on the difference and convergence between IFRS and US GAAP concludes this paper.

Risk of neuropsychiatric symptoms among people who develop cognitive impairment with and without a history of post‐traumatic stress disorder

AbstractBackgroundMost research studying the link between PTSD and cognitive impairment focuses on PTSD as a risk factor for developing Alzheimer’s disease; however, few studies focus on the impact cognitive impairment has on neuropsychiatric symptoms among people with a history of PTSD. Symptoms associated with cognitive decline may increase or cause the relapse of recurrent intrusive memories, dreams, or flashbacks of traumatic events among individuals with a history of PTSD. Additionally, cognitive impairment may lead to a delayed onset, worsening, or recurrence of PTSD‐related neuropsychiatric symptoms among people with PTSD. We aimed to prospectively assess the change in neuropsychiatric symptoms among people who develop cognitive impairment and have a history of PTSD.MethodData were obtained from the National Alzheimer’s Coordinating Center (NACC). Eligibility criteria included participants who were cognitively unimpaired and had non‐missing assessment of PTSD at initial visit, and who had at least one follow‐up visit with cognitive impairment. Information from the Uniform Data Set (UDS) health history and clinical diagnosis forms at the initial visit was used to separate participants with a history of (PTSD+) and participants without a history of (PTSD‐). Demographics, clinical symptoms, and mean NPI‐Q severity scores at the first UDS visit vs first visit with a Clinical Dementia Rating global score (CDR‐GS) of 0.5 were compared between groups.ResultThe final analytic sample included 18 PTSD+ participants and 585 PTSD‐ participants. While both groups experienced significantly higher mean NPI‐Q severity scores between baseline and the first visit with CDR‐GS = 0.5, there was not a significant difference between the groups (p = 0.79). Additionally, both groups exhibited significantly increased rates of change in NPI‐Q severity scores, but this rate was not statistically different between the groups (p = 0.73).ConclusionIn this sample, we found no differences in NPI‐Q scores. The small sample size of PTSD+ participants as well as the mild nature of participants’ cognitive impairment might account for these null findings, as it might be too mild to impact PTSD symptoms; as such, future research examining these symptoms in larger and more diverse samples is warranted.

Measurement Invariance of the NIH Toolbox‐Cognition Battery Across Demographic Characteristics

AbstractBackgroundThe tablet‐based NIH Toolbox‐Cognition Battery (NIHTB‐CB) was compared against the established NACC Uniform Data Set 3.0 (UDS 3.0) neuropsychological battery (used across Alzheimer’s Disease Research Centers). NIHTB‐CB factor invariance is critical for drawing appropriate cross‐demographic conclusions. Invariance was assessed across both batteries for consensus‐driven clinical diagnosis, sex, and age in a large sample of participants from the Advancing Reliable Measurement in Alzheimer’s Disease and Cognitive Aging (ARMADA) study. Goals of this project were to establish the factor relationship of these two batteries and examine their invariance across the aforementioned demographic characteristics.MethodNIHTB‐CB tasks measure Crystalized and Fluid ability. Tests were administered to 571 participants aged 65 to 99 years (mean age 77.2±7.8, 41% female) with normal cognition or amnestic mild cognitive impairment. Groups were classified for age (< = 85 and > 85), gender, and diagnosis. Exploratory Factor Analysis (EFA) was tested for each group with confirmatory factor analysis. If the model fit sufficiently between groups for the configural model, then metric, scalar, and strict invariance models were also examined. Uncorrected standard scores were used for analyses.ResultA four‐factor model emerged as the best fitting model and was invariant across all demographic groups through adjustments. Factor 1: Language (MINT Total, Oral Reading, Picture Vocabulary); Factor 2: Auditory retention (Digits Forward Total and Digits Backward Total); Factor 3: Learning/Memory (List Sorting Working Memory, Picture Sequence Memory, Craft Story 21 Immediate Recall, Craft Story 21 Delayed Recall, Benson Delayed Recall, Animals Category, Vegetables Category, and MoCA Total Score); Factor 4: Attention/Executive Functioning (Dimensional Card Sort, Flanker, Trails A, Trails B, Pattern Comparison Processing Speed, and Benson Recall). We found strong support for strict invariance across the assessed demographic characteristics.ConclusionA four‐factor structure comprised of NIHTB‐CB and UDS 3.0 tests supports the validity of the NIHTB‐CB by matching both UDS 3.0 and NIHTB‐CB measures as expected. Strong support for strict invariance reflects the psychometric equivalence of the NIHTB‐CB construct across the groups. The psychometric properties of the NIHTB‐CB subtests therefore are generalizable across samples. By satisfying the assumption of measurement invariance, inferences made using the NIHTB‐CB about between‐group differences are presumed valid.

Normative data on the CERAD Word List in a robustly intact sample of older adults

AbstractBackgroundList learning and memory tests are frequently used to evaluate older adults for late life cognitive disorders, such as Alzheimer’s disease. The Word List subtest of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) neuropsychological battery has been widely‐used in clinical research, including clinical trials, to identify and track such disorders. It is also freely‐available, as it is in the public domain. However, normative data, especially in robustly intact older adults, is lacking.MethodCERAD Word List data from 897 older adults (age in years: M = 81.1, SD = 8.1, range 55‐100; 67% women; education in years: M = 14.8, SD = 2.8, range = 7‐24) deemed to be cognitively intact at two annual visits in a longitudinal observational study from the Uniform Data Set of the National Alzheimer’s Coordinating Center were used to generate predicted Word List scores based on age, sex, and education. Predicted scores were compared to observed scores to preliminarily validate these normative values.ResultFor six primary scores from the Word List subtest (Trial 1, Trial 2, Trial 3, Total Acquisition, Delay Recall, Recognition Total Correct), the three demographic variables significantly predicted test scores, with R2s ranging from 0.03 – 0.18 (p‐values<0.001). When these predicted scores were compared to observed scores, there were no significant differences (p‐values>0.40), indicating preliminary support for these prediction scores.ConclusionAlthough further validation is needed in independent intact and impaired samples, the current demographically‐based prediction scores provide normative data for the CERAD Word List subtest for older adults being evaluated for cognitive complaints. The inclusion of additional variables needs to be examined to improve the accuracy of these prediction scores. Nonetheless, normative data allows providers and researchers to be confident in using this learning and memory test in clinics and clinical trials.

Preliminary neurocognitive finding from a multi‐site study investing long‐term neurological impact of COVID‐19 using ultra‐high field 7 Tesla MRI‐based neuroimaging

AbstractBackgroundGlobally, over six hundred million cases of SARS‐CoV‐2 have been confirmed. As the number of individuals in recovery rises, examining long‐term neurological effects, including cognitive impairment and cerebral microstructural and microvascular changes, has become paramount., We present preliminary cognitive findings from an ongoing multi‐site study investigating the long‐term neurological impacts of COVID‐19 using 7 Tesla MRI‐based neuroimaging.MethodsAcross 3 US and 1 UK sites, we identified adult (> = 18) COVID‐19 survivors (CS) and healthy controls (HC) without significant pre‐existing medical, neurological, or psychiatric illness. Using the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS‐3) battery and Norms Calculator, 12 cognitive scores were adjusted for age, sex, and education and classified as either unimpaired or mild (<9th percentile), moderate (<2nd percentile), or severely impaired (<1st percentile). The observed frequency of impairment across the two groups is reported along with proportional differences (PD) and confidence intervals (CI). Illness severity and time since infection were evaluated as potential associates of cognitive impairment.ResultsOver a period of 11 months, we enrolled 108 participants. At the time of reporting, 80 (46.3% female; mean age: 60.3 ± 8.6; 61 CS, 19 HC) had completed cognitive assessments. Of the participants for whom we ascertained time since symptom onset and illness severity (n = 51 and 43, respectively), 31.4% had their index COVID‐19 infection within the past year, and 60.5% had a severe to critical infection (Table 1). Table 2 reports observed frequency of impairment for each metric. Aggregating all 12 cognitive metrics, we found 45 (73.8%) of CS had at least one impairment [vs HC: 10 (52.6%)]. A significantly greater proportion of CS had at least one moderate to severe or severe impairment (Figure 1). CS also had significantly higher frequencies of presenting with two or more mild to severe impairments [PD 0.33 (0.13, 0.54)]. Illness severity and time since infection were not significantly associated with cognitive impairment.ConclusionOur preliminary results are consistent with potentially sustained COVID‐associated cognitive impairment in a subset of participants. Enrollment in the multi‐site cohort is ongoing, and updated results will be presented along with ultra‐high field MRI‐based neuroimaging correlates.

Harmonization of amyloid and tau PET tracers in a multi‐center cohort from NIA Alzheimer’s disease research centers in the United States

AbstractBackgroundThe Standardized Centralized Alzheimer’s & related dementias Neuroimaging (SCAN) project harmonizes processing of prospectively acquired PET and MRI scans from Alzheimer’s disease research centers (ADRCs) across the USA. SCAN data will be integrated with other ADRC data streams within the National Alzheimer’s Coordinating Center (NACC), including the Uniform Data Set which includes rich longitudinal demographic, cognitive, and neuropathological data, resulting in a large, potentially representative cohort. The project has sought to accommodate site‐to‐site variation in image acquisition while permitting harmonization of imaging data, necessitating new analytic approaches for different PET tracers and PET scans without MRIs.MethodPET scans underwent preprocessing for quality control and smoothing to a uniform resolution. MRI‐free processing pipelines and positivity thresholds were developed for each beta‐amyloid (Aβ)‐PET tracer: [11C]PiB, [18F]florbetaben (FBB), [18F]florbetapir (FBP) and [18F]flutafuranol (NAV). A generic Aβ‐PET template was used for spatial normalization and cortical summary SUVRs were calculated in template space. Aβ‐positivity thresholds were determined by examining both MRI‐dependent thresholds transformed into MRI‐free units and a young control mean+2SD. Centiloid (CL) conversions were completed for each Aβ‐PET tracer. Tau‐PET tracers ([18F]flortaucipir (FTP) and [18F]MK‐6240) required tracer‐specific warping templates for best performance relative to gold‐standard MRI‐dependent processing. Demographic and clinical data were obtained from NACC.Result15 ADRCs have contributed data to SCAN as of January 2023. 477 participants had preprocessed, SCAN‐compliant (https://scan.naccdata.org/) Aβ‐PET scans and NACC data; 219 of these participants (46%) also had tau‐PET scans (Table 1). This cohort included 18% of individuals from underrepresented groups. Clinical diagnostic groups ranged from cognitively unimpaired to MCI and dementia (74% of whom had AD dementia). Using pooled Aβ‐PET data across 4 tracers in CLs, there were expected differences in Aβ burden by diagnosis group (Fig. 1) and positive associations with tau (Fig. 2).ConclusionSCAN has successfully harmonized PET data from more than a dozen US ADRCs yielding a large, clinically heterogeneous cohort of deeply phenotyped research participants. SCAN data will be compatible with ADNI data allowing for further pooling of data and greater statistical power to drive critical questions in AD and related dementias research.

Clinical Manifestations.

Balancing the need to standardize cognitive measures with the need to evolve alongside advances in scientific knowledge is an inherent challenge for longitudinal studies of Alzheimer disease (AD). The Uniform Data Set (UDS) offers a solution for this in cognitive batteries and is implemented across Alzheimer Disease Research Centers (ADRCs). Using equipercentile equating, we combined data spanning two versions of the UDS and derived domain-specific and general cognitive composites. We compared the relationships of these measures with common biomarkers of AD, as well as their statistical power to detect cognitive change in clinical trials. Using longitudinal data from the Knight ADRC, domain-specific cognitive composites were created using an exploratory factor analysis. The resulting four factors clustered episodic memory, semantic memory, working memory, and attention/processing speed tasks (Figure 1). We also computed a commonly-used preclinical Alzheimer cognitive composite (PACC), an alternative Knight-ADRC-PACC, which did not include MMSE, and a global composite. Relationships between each cognitive composite and neuroimaging metrics of amyloid, tau, atrophy, and clinical symptoms, were compared cross-sectionally and longitudinally (Figure 2). We also performed power analyses of all composites to calculate necessary sample sizes to observe a medium-sized effect in a four-year clinical trial. Overall, general metrics of cognition slightly outperformed domain-specific composites, although the working memory composite consistently reflected a poor relationship with AD biomarkers (Figure 2). Power analyses revealed that the global, Knight-ADRC-PACC, and attention/processing speed composites would require the smallest sample sizes to detect cognitive change in a clinical trial, while the ADCS-PACC, required two to three times as many individuals (Figure 3). We found that data-driven, domain-specific, composites of cognitive performance can complement already-established general cognitive composites, with the possible exception of the present working memory composite. In line with prior work, we highlight that attention and processing speed may be sensitive metrics of cognitive change in AD. The inclusion of the MMSE, such as in the ADCS-PACC, may greatly reduce the sensitivity and statistical power of summary cognitive composites. Our results have important implications for the development of cognitive outcome metrics for future clinical trials.

Comparison of Baseline Cognitive Testing Results in Mild Cognitive Impairment Subtypes in the NACC Database

AbstractBackgroundMild cognitive impairment (MCI) reflects cognitive deficits without significant functional decline in activities of daily living. MCI subtypes correspond to phenotypic cognitive impairment patterns in memory, attention, language, executive, and visuospatial compared to age‐adjusted norms: Amnestic single domain (ASD) or multi‐domain (AMD), Nonamestic single domain (NASD), or Nonamnestic multi‐domain (NAMD). Previous studies have suggested Nonanmestic subtypes demonstrate more likely represent non‐AD prodromes, but conclusions have been limited by small sample sizes. The present study attempts to see correlates of htese proposed differences as evident by cognitive testing battery.MethodUsing the National Alzheimer’s Coordinating Center (NACC) database (grant U24 AG072122), we selected all initial Uniform Data Set (UDS) assessments diagnosed as MCI with subtyping. The NACC data was collected from 37 participating Alzheimer’s disease Research Centers. Baseline demographic, clinical features, Neuropsychiatric symptoms, and AD biomarkers were collected. One‐way ANOVA analyses were used to compare cognitive performance on all UDS cognitive tests across MCI subtypes.Result9852 individuals with MCI on baseline UDS visits were analyzed: 45.4% AMD, 33.7% ASD, 12.6% NASD, 8.2% NAMD. There were significant differences (P<.001 or greater) on all measures which is not unexpected given large sample size. Notable differences between ASD/AMD, NASD/NAMD were prominent on Trails B, Craft story immediate and delayed recall, and Benton immediate recall minus delayed recall. See table for complete summary of scores.Trails B, mean (SD) times were ASD (113±62), AMD (157±82), NASD (143±78),NAMD (171±83). Craft story showed clear differences between groups on immediate recall (ASD14.9 (6.4)/AMD 14.2 (6.76)/NASD 18.0 (8.0/NAMD 17.9 (6.9)) and delayed recall (ASD 9.9 (7.0)/AMD 10.1 (6.7)/NASD 15.2 (6.4)/NAMD 15.5 (6.2)). Benton immediate minus delayed recall differences were ASD 8.1/AMD 7.9 versus NASD 4.2, NAMD 4.9)ConclusionNonamnestic MCI subtypes are characterized by lower performance on executive tasks such as Trails B. Conversely, memory tasks show lower performance in NACC UDS participants with amnestic subtypes of MCI. This would support the concept that Nonamnestic MCI may represent prodromal stages of non‐AD dementia more frequently.

Primary etiology and associated medical conditions in Mild Cognitive Impairment Subtypes in the NACC Database

AbstractBackgroundMild cognitive impairment (MCI) reflects cognitive deficits without significant functional decline in activities of daily living (1). MCI subtypes correspond to phenotypic cognitive impairment patterns in memory, attention, language, executive, and visuospatial compared to age‐adjusted norms: Amnestic single domain (ASD) or multi‐domain (AMD), Nonamnestic single domain (NASD), or Nonamnestic multi‐domain (NAMD). Previous studies have suggested nonamnestic subtypes are more likely to represent non‐AD prodromes, but conclusions have been limited by small sample sizes (1).MethodUsing the National Alzheimer’s Coordinating Center (NACC) database (grant U24 AG072122), we selected all initial Uniform Data Set (UDS) assessments diagnosed as MCI with subtyping. The NACC data was collected from 37 participating Alzheimer’s disease Research Centers. Baseline clinical features, primary etiology and associated medical conditions were collected.Results9852 individuals with MCI on baseline UDS visits were analyzed: 45.6% were diagnosed with AD (Table 1). AD was the most common primary etiology in all subtypes, more frequent in amnestic than nonamnestic subtypes (AMD 54.1%; ASD 50.5%; NAMD 20.0%; NASD 18.8%). Overall, 6% were diagnosed with DLB and 3.1% with PD; NAMD and NASD were more frequent than AMD or ASD. The most frequently associated medical conditions were hypercholesterolemia, HTN, depression, anxiety, sleep apnea and diabetes (table 2). The highest percentage of depression and anxiety were in nonamnetsic subtypes. Recent stroke was most frequent in NAMD.ConclusionMCI is a heterogeneous disorder, and its classification by subtypes is essential in understanding prodromal stages and neurodegenerative neuropathology. Dementia risk modulation and prevention is linked to identification and treatment of associated medical conditions including frequent neuropsychiatric comorbidity (2).

Identifying change on the CERAD Word List with standardized regression‐based scores

AbstractBackgroundList learning and memory tests are frequently used to evaluate older adults for Mild Cognitive Impairment and Alzheimer’s disease. One such test, the Word List subtest of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), has been widely‐used in clinical trials track such disorders. However, data is lacking on the amount of change that is needed to accurately identify decline.MethodParticipants included 897 older adults (age in years: M = 81.1, SD = 8.1, range 55‐100; 67% women; education in years: M = 14.8, SD = 2.8, range = 7‐24) who were cognitively intact at two annual visits in a longitudinal observational study from the Uniform Data Set of the National Alzheimer’s Coordinating Center. Each participant completed the Word List subtest at two visits, which were approximately 1 year apart (interval in years: M = 1.3, SD = 1.0). Multiple linear regression predicted Time 2 scores from Time 1 scores and simple demographic variables (age, sex, education). The predicted Time 2 scores were compared to observed Time 2 scores to preliminarily validate this change data.ResultFor three primary scores from the Word List subtest (Acquisition Total Correct, Delay Recall Total Correct, Recognition Total Correct), the Time 2 scores were significantly predicted from the other variables, with R2s ranging from 0.16 – 0.41 (p‐values<0.001). When these predicted Time 2 scores were compared to observed Time 2 scores, there were no significant differences (p‐values≧0.19), indicating preliminary support for these prediction scores.ConclusionThe current prediction equations appear to allow clinicians and researchers to accurately track change on the Word List subtest of the CERAD by comparing their patients/participants to the current sample of robustly intact older adults. Consistent with past work in this area, Time 1 cognitive scores were the best predictors of Time 2 cognitive scores, although demographic variables added to that prediction. Future work should consider if additional variables can improve the prediction equations. Future work should also validate these equations in independent samples to examine their clinical utility.

The 40 pc sample of white dwarfs from Gaia

ABSTRACT We present a comprehensive overview of a volume-complete sample of white dwarfs located within 40 pc of the Sun, a significant proportion of which were detected in Gaia Data Release 3 (DR3). Our DR3 sample contains 1076 spectroscopically confirmed white dwarfs, with just five candidates within the volume remaining unconfirmed (> 99 per cent spectroscopic completeness). Additionally, 28 white dwarfs were not in our initial selection from Gaia DR3, most of which are in unresolved binaries. We use Gaia DR3 photometry and astrometry to determine a uniform set of white dwarf parameters, including mass, effective temperature, and cooling age. We assess the demographics of the 40 pc sample, specifically magnetic fields, binarity, space density, and mass distributions.

97 Evaluation of Video and Telephone-Based Administration of the Uniform Data Set Version 3 (UDS v3.0) Teleneuropsychological Measures

Objective:Telecommunication-assisted neuropsychological assessment (teleNP) has become more widespread, particularly in response to the COVID-19 pandemic. However, comparatively few studies have evaluated in-home teleNP testing and none, to our knowledge, have evaluated the National Alzheimer’s Coordinating Center’s (NACC) Uniform Data Set version 3 tele-adapted test battery (UDS v3.0 t-cog). The current study compares in-home teleNP administration of the UDS v3.0, acquired while in-person activities were suspended due to COVID-19, with a prior in-person UDS v3.0 evaluation.Participants and Methods:210 participants from the Michigan Alzheimer’s Disease Research Center’s longitudinal study of memory and aging completed both an in-person UDS v3.0 and a subsequent teleNP UDS v3.0 evaluation. The teleNP UDS v3.0 was administered either via video conference (n = 131), telephone (n = 75), or hybrid format (n = 4) with approximately 16 months between evaluations (mean = 484.7 days; SD = 122.4 days; range = 320-986 days). The following clinical phenotypes were represented at the initial assessment period (i.e., the most recent in-person UDS v3.0 evaluation prior to the teleNP UDS v3.0): cognitively healthy (n = 138), mild cognitive impairment (MCI; n = 60), dementia (n = 11), and impaired not MCI (n = 1). Tests included both the in-person and teleNP UDS v3.0 measures, as well as the Hopkins Verbal Learning Test-Revised (HVLT-R) and Letter “C” Fluency.Results:We calculated intraclass correlation coefficients (ICC) with raw scores from each time point for the entire sample. Sub-analyses were conducted for each phenotype among participants with an unchanged consensus research diagnosis: cognitively healthy (n = 122), MCI (n = 47), or cognitively impaired (i.e., MCI, dementia, and impaired not MCI) (n = 66). Test-retest reliability across modalities and clinical phenotypes was, in general, moderate. The poorest agreement was associated with the Trail Making Test (TMT) - A (ICC = 0.00; r = 0.027), TMT - B (ICC = 0.26; r = 0.44), and Number Span Backward (ICC = 0.49). The HVLT-R demonstrated moderate reliability overall (ICC = 0.51-0.68) but had notably weak reliability for cognitively healthy participants (ICC = 0.12-0.36). The most favorable reliability was observed in Craft Story 21 Recall - Delayed (ICC = 0.77), Letter Fluency (C, F, and L) (ICC = 0.74), Multilingual Naming Test (MINT) (ICC = 0.75), and Benson Complex Figure – Delayed (ICC = 0.79).Conclusions:Even after accounting for the inherent limitations of this study (e.g., significant lapse of time between testing intervals), our findings suggest that the UDS v3.0 teleNP battery shows only modest relationships with its in-person counterpart. Particular caution should be used when interpreting measures showing questionable reliability, though we encourage further investigation of remote vs. in-person testing under more controlled conditions.

37 Perceived Financial Exploitation Vulnerability is Associated with Morphometry of the Parahippocampal Gyrus and Entorhinal Cortex in Cognitively Normal Older Adults

Objective:Prior work suggests financial exploitation vulnerability may be an early behavioral manifestation of Alzheimer’s disease (AD). Brain morphometric measures of the parahippocampal gyrus and entorhinal cortex have been shown to be sensitive to early AD progression. We hypothesized that perceived financial exploitation vulnerability may be associated with morphometric measures of the parahippocampal gyrus and entorhinal cortex in cognitively unimpaired older adults. We secondarily investigated the association of morphometric measures with neuropsychological measures.Participants and Methods:The sample consisted of 39 cognitively unimpaired older adults (mean age = 68.74 ± 6.43, mean education = 16.87 ± 2.35, 77% female). Cognitive impairment was screened using the telephone version of the Montreal Cognitive Assessment (MoCA) and the cut-off was 21 for study participation. Perceived financial exploitation vulnerability was characterized using a 6-item self-report measure derived from the contextual items of the Lichtenberg Financial Rating Scale. Neuropsychological measures included the CVLT-II Long Delay Free Recall (verbal memory), Benson Complex Figure Recall (visual memory), and Verbal Fluency: Phonemic Test from the Alzheimer’s Disease Centers’ Uniform Data Set (UDS) version 3. Brain images were collected on a 7 Tesla Siemens Magnetom with the following parameters: TE=2.95ms, TR=2200ms, 240 sagittal slices, acquired voxel size (avs)=0.7mm x 0.7mm x 0.7mm. Structural brain images were processed using FreeSurfer version 7.2.0. Cortical thickness and volume measures were generated using the Killiany/Desikian parcellation atlas. Regions were averaged across hemispheres to obtain a single value for each region. Volume measures were adjusted for intracranial volume. Bivariate analyses were conducted to assess relationships between each outcome variable and potential confounders (age, sex, and education). Linear regression models were adjusted for any covariates significantly associated with the outcome variable (none for perceived financial exploitation vulnerability; sex and age for verbal memory; education for visual memory; sex for verbal fluency).Results:Smaller entorhinal cortex volume (β = -1275.14, SE = 582.79, p < 0.05) and lower parahippocampal gyrus thickness (β = -3.37, SE = 1.57, p < 0.05) were significantly associated with greater perceived financial exploitation vulnerability. Lower entorhinal cortex thickness was marginally associated with greater perceived financial exploitation vulnerability (β = -2.03, SE = 1.11, p = 0.08). Higher parahippocampal gyrus thickness was associated with better verbal fluency (β = 17.66, SE = 7.01, p < 0.05). Higher entorhinal cortex thickness was associated with better visual memory (β = 4.71, SE = 1.73, p < 0.05). No significant associations were observed between structural brain measures and verbal memory.Conclusions:These results suggest smaller entorhinal cortex volume and lower parahippocampal gyrus thickness are associated with higher perceived financial exploitation vulnerability in cognitively normal older adults. Additionally, parahippocampal gyrus thickness appears to be associated with verbal fluency abilities while entorhinal cortex thickness appears to be associated with visual memory. Taken together, these findings lend support to the notion that financial exploitation vulnerability may serve as an early behavioral manifestation of preclinical AD. Longitudinal studies are needed to better understand the temporal nature of these relationships.

1 Associations Between Alcohol-Related Problems, Neuropsychological Measures, and Financial Exploitation Vulnerability in a Low-Drinking Sample of Cognitively Unimpaired Older Adults

Objective:In recent years, rates of alcohol consumption and alcohol use disorder have steadily increased among adults age 60 and older. Large studies have demonstrated that moderate-to-heavy alcohol consumption (>7 drinks per week) is a risk factor for developing various types of dementias. The effects of alcohol-related problems on cognition are less clear, and are particularly understudied in older adults. Similarly, while there is an established link between worse cognition and financial exploitation vulnerability in older adults, no studies have examined relationships between alcohol-related problems and financial exploitation in this population. The current study therefore explores whether alcohol-related problems are associated with neuropsychological performance and financial exploitation vulnerability in a sample of older adults.Participants and Methods:Participants were a community sample of cognitively unimpaired adults over the age of 50 (N = 55, Age M(SD) = 69.1(6.2), 74.5% female, Years of education M(SD) = 16.8(2.3)). Interested individuals were excluded if they reported current or past substance use disorders. Participants completed a laboratory visit that included a neuropsychological assessment. Measures included the NIH Cognition toolbox, CVLT-II, Digit Span, Trails A/B, Benson Complex Figure Recall, and Verbal Fluency: Phonemic and Semantic, from the Alzheimer’s Disease Centers’ Uniform Data Set (UDS) version 3. Participants completed the CAGE Alcohol Abuse Screening Tool and the Short Michigan Alcohol Screener Test - Geriatric Version (SMAST) to assess alcohol-related problems. Both measures are used as clinical screening tools to measure likelihood of a substance use disorder and produce a summary score (0-4 for CAGE, 010 for SMAST) tabulating symptoms of alcohol-related problems. Participants also completed the Perceived Financial Vulnerability Scale (PFVS) to assess financial exploitation vulnerability. As a significant number of participants reported no drinking and therefore no alcohol-related problems, negative binomial regressions were used to test associations between neuropsychological measures, financial exploitation vulnerability, and alcohol-related problems.Results:After covarying for age and sex, SMAST was negatively associated with NIH toolbox total cognition (B(SE) = -.14(.07), p<.05) and marginally negatively associated with fluid cognition (B(SE) = -.07(.04), p=.06). Neither SMAST nor CAGE scores were significantly associated with performance on any other neuropsychological test (ps = .13-.99). SMAST was positively associated with financial exploitation vulnerability (B(SE) = .31(.16), p = .05); this effect remained significant after covarying for NIH total composite score in a secondary analysis.Conclusions:In a community sample of cognitively unimpaired, low-drinking adults over the age of 50, more alcohol-related problems were associated with worse NIH toolbox cognition scores. Similarly, more alcohol-related problems were associated with greater financial exploitation vulnerability, and this relationship was not driven by worse cognition. These results suggest that even low amounts of drinking and alcohol-related problems may be associated with cognition and financial exploitation vulnerability in cognitively unimpaired older adults. This study also corroborates the use of the SMAST over the CAGE in older adult populations that may be more sensitive to cognitive changes.

98 On Combining In-Person and Remote National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS) data

Objective:Although remote neuropsychological assessments have become increasingly common, current research on the reliability and validity of scores obtained from remote at-home assessments are sparse. No studies have examined remote at-home administration of the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS) even though this battery is being used to track over 45,000 participants over time. This study aimed to determine whether remote UDS scores can be combined with in-person data by assessing whether rates of score changes over time (i.e., reliability) differed by modality and whether remote and in-person scores converge (i.e., validity).Participants and Methods:Data for UDS visits conducted from 09/2005 to 12/2021 from 43 Alzheimer’s Disease Research Centers were examined. We identified 311 participants (254 cognitively unimpaired, 7 impaired - not mild cognitive impairment, 25 mild cognitive impairment, 25 dementia) who completed 2 remote UDS visits 0.868 years apart (SD = 0.200 years). First, initial remote scores were correlated with most recent in-person scores. Second, we examined whether rates of change differed between remote and in-person assessments. Repeated-measure one-way ANOVA were used to compare rates calculated from the same individual from remote versus inperson assessments. We additionally identified a demographically- and visit-number-matched group of 311 participants with in-person UDS visits given that all remote visits occurred after in-person visits; one-way ANOVAs were used to compare remote rates to rates from in-person assessments from the matched in-person group. Finally, accuracy of remote scores were assessed by quantifying the difference between the actual remote scores and predicted scores based on repeated in-person assessments. These residual values were then divided by the maximum score to form error rates.Results:Remote UDS score on MoCA-blind, Craft story immediate and delayed recall, digits forward, digits backward, phonemic fluency (F, L, F + L), and semantic fluency (animals, vegetables, animals + vegetables) were all highly correlated (all ps < 0.001) with scores obtained from preceding in-person assessments. At the group level, within-subject comparisons between remote and in-person rates of change were not significantly different for 7/11 tests; between-subject comparisons were not significantly different for 10/11 tests. Vegetable fluency had slightly reduced rates of change with remote assessment compared to inperson assessments. Critically, remote scores were consistent with predicted scores based on the trajectory of each subject’s in-person assessments with group mean error rates ranging from 0.7% (Craft Delayed Recall) to 3.9% (Phonemic fluency - F).Conclusions:Our results demonstrate adequate reliability and convergent validity for remotely administered verbally based tests from the NACC UDS battery. Importantly, our findings provide some support for combining remote and in-person scores for studies that transitioned to remote testing due to COVID-19. However, future research is needed for tests with visual stimuli that assess visual memory, visuospatial function, and aspects of executive function.

97 Exploring Urban-Rural Disparities in Alzheimer’s disease: Clinical characterization of a southern Nevada cohort

Objective:As the US population ages, the prevalence of Alzheimer’s disease and related dementias (AD/RD) is on the rise. This is especially true in rural America, where mortality rates due to AD/RD are rising faster than in metropolitan areas. To date, however, people living in rural communities are severely underrepresented in aging research. The Nevada Exploratory Alzheimer’s Disease Research Center (NVeADRC) seeks to address this gap. Here, we present preliminary cognitive data from our rural-dwelling cohort, as well as relevant demographic and clinical characteristics.Participants and Methods:Individuals with normal cognition (NC), mild cognitive impairment (MCI), and dementia due to Alzheimer’s disease (AD) living in rural communities, defined as a rural-urban commuting area (RUCA) code of 4 or higher, were enrolled through either clinic or community outreach. Eligibility for the observational cohort required: age >55 years, primarily English-speaking, primary residence in a rural community, and availability of a study partner. Measures included the Uniform Data Set (v3), blood-based biomarkers, structural brain MRI, and portions of the PhenX Social Determinants of Health toolkit. Participants are seen at baseline and followed annually, with interim remote visits every 6 months. A multidisciplinary consensus diagnosis is rendered after each visit. Where feasible, a harmonized urban cohort followed by the Nevada Center for Neurodegeneration and Translational Neuroscience (CNTN) was used for comparison.Results:Fifty-six rural-dwelling (age=70.4±7.1 years; edu=15.2±2.6 years; 61% female) and 148 urban-dwelling (age=72.9±6.8 years; edu=15.8±2.7 years; 46% female) older adults were included; age significantly differed between cohorts but education did not. The rural cohort was 46% NC (MoCA=26.8±2.3; CDRsob=0.3±0.6), 32% MCI (MoCA=22.8±3.1; CDRsob=1.2±1.0), and 22% AD (MoCA=16.9±5.5; CDRsob=5.2±3.0). The urban cohort was 39% NC (MoCA=26.4±2.6; CDRsob=0.3±0.8), 44% MCI (MoCA=22.3±3.1; CDRsob=2.0±1.5) and 17% AD (MoCA=18.6±3.9; CDRsob=4.7±2.3). Rural communities were significantly more disadvantaged, as measured by the Area Deprivation Index (ADI), than urban communities (rural ADI=6.3±2.6; urban ADI=3.4±2.3; p<.001). Fifty-percent of the rural cohort lives in a moderate to severely disadvantaged neighborhood (ADI Decile>7) compared to 12% of the urban cohort, and 11% of individuals in the rural cohort reported living more than 30 miles from the nearest medical facility. Across the combined cohort, education was significantly correlated with ADI deciles (r=-.30, p<.001), with people in the areas of highest disadvantage having the lowest education. Verbal memory was also inversely associated with ADI. There were no differences in clinical diagnosis as a function of ADI rank.Conclusions:Living in a rural community conveys a multifaceted array of risks and benefits, some of which differ from urban settings. The literature to date suggests that older adults living in rural communities are at significantly increased risk for morbidity and mortality due to AD/RD, though it is unclear why. Preliminary data from the NVeADRC show that increasing levels of neighborhood disadvantage were associated with lower levels of education and worse verbal memory in this convenience sample. The combined effect of low education and increased disadvantage account for some of the urban-rural differences in mortality that have been reported, though additional research on representative samples in this underrepresented population is critical.

34 Machine Learning Predicts Time to Dementia Conversion in Cognitively Normal Subjects

Objective:Identification of pre-clinical Alzheimer’s disease (AD) is necessary for the development of future disease-modifying treatments, which would ideally target pre-clinical stages to mitigate functional loss. Despite advanced in biomarker development, clinical trials are still without a non-invasive and cost-effective means of identifying pre-symptomatic subjects who are at high risk for eventual conversion to AD. In previous work, we developed a machine learning algorithm using neuropsychological test scores and health history to identify subjects at high risk for eventual conversion. Here, we examine the performance of a similar algorithm in predicting the timing of that conversion in years.Participants and Methods:Data were obtained from the National Alzheimer’s Coordination Center (NACC) Uniform Data Set (UDS) version 3.0. Subjects with normal cognition at baseline were used to train a multi-class Random Forest classifier to predict conversion to AD. Each subject could be classified as a short-, mid-, or long-term converter (0-3 years, 4 to 6 years, and 7 to 9 years, respectively) or as a non-converter, if no dementia diagnosis was given within ten years of baseline. Predictors included baseline demographics, basic medical history, and neuropsychological test results. Algorithms were evaluated using standard, cross-validated performance metrics.Results:Multi-class Matthews correlation coefficient between predicted time to diagnosis and the ground truth averaged 0.26 +/- 0.06 across 100 cross validation splits. Prediction accuracy exceeded 0.67 in all cases, when computed for each class individually, and was greatest for the short-term (0.75) and nonconverter (0.78) classes.Conclusions:Machine-learning algorithms applied to neuropsychological, demographic, and medical history information were able to predict the eventual timing of conversion to dementia in cognitively healthy adults significantly better than chance. Results were most accurate when predicting shorter time to conversion. Results illustrate the potential of this data analytic approach for targeted recruitment in clinical trials.

51 Feasibility of Remote Administration of a Modified UDSv3 Cognitive Battery

Objective:Face-to-face administration is the “gold standard” for both research and clinical cognitive assessments. However, many factors may impede or prevent face-to-face assessments, including distance to clinic, limited mobility, eyesight, or transportation. The COVID19 pandemic further widened gaps in access to care and clinical research participation. Alternatives to face-to-face assessments may provide an opportunity to alleviate the burden caused by both the COVID-19 pandemic and longer standing social inequities. The objectives of this study were to develop and assess the feasibility of a telephone- and video-administered version of the Uniform Data Set (UDS) v3 cognitive batteries for use by NIH-funded Alzheimer’s Disease Research Centers (ADRCs) and other research programs.Participants and Methods:Ninety-three individuals (M age: 72.8 years; education: 15.6 years; 72% female; 84% White) enrolled in our ADRC were included. Their most recent adjudicated cognitive status was normal cognition (N=44), MCI (N=35), mild dementia (N=11) or other (N=3). They completed portions of the UDSv3 cognitive battery, plus the RAVLT, either by telephone or video-format within approximately 6 months (M:151 days) of their annual in-person visit, where they completed the same in-person cognitive assessments. Some measures were substituted (Oral Trails for TMT; Blind MoCA for MoCA) to allow for phone administration. Participants also answered questions about the pleasantness, difficulty level, and preference for administration mode. Cognitive testers provided ratings of perceived validity of the assessment. Participants’ cognitive status was adjudicated by a group of cognitive experts blinded to most recent inperson cognitive status.Results:When results from video and phone modalities were combined, the remote assessments were rated as pleasant as the inperson assessment by 74% of participants. 75% rated the level of difficulty completing the remote cognitive assessment the same as the in-person testing. Overall perceived validity of the testing session, determined by cognitive assessors (video = 92%; phone = 87.5%), was good. There was generally good concordance between test scores obtained remotely and in-person (r = .3 -.8; p < .05), regardless of whether they were administered by phone or video, though individual test correlations differed slightly by mode. Substituted measures also generally correlated well, with the exception of TMT-A and OTMT-A (p > .05). Agreement between adjudicated cognitive status obtained remotely and cognitive status based on in-person data was generally high (78%), with slightly better concordance between video/in-person (82%) vs phone/in-person (76%).Conclusions:This pilot study provided support for the use of telephone- and video-administered cognitive assessments using the UDSv3 among individuals with normal cognitive function and some degree of cognitive impairment. Participants found the experience similarly pleasant and no more difficult than inperson assessment. Test scores obtained remotely correlated well with those obtained in person, with some variability across individual tests. Adjudication of cognitive status did not differ significantly whether it was based on data obtained remotely or in-person. The study was limited by its’ small sample size, large test-retest window, and lack of randomization to test-modality order. Current efforts are underway to more fully validate this battery of tests for remote assessment. Funded by: P30 AG072947 & P30 AG049638-05S1